The long-term objective of this research is to establish ultrasound as a safe, effective, and non-invasive method for assessing fracture risk, an important component in clinical management of osteoporosis. Osteoporosis afflicts over 20 million people in the U.S., responsible for more than 275,000 hip fractures annually. Currently, the primary means for assessment relies on densitometric techniques. These methods subject the patient to ionizing radiation, are relatively expensive, and do not always provide good estimates of bone strength. Ultrasound offers several potential advantages. It is non-ionizing and relatively inexpensive. Moreover, since ultrasound is a mechanical wave and interacts with bone in a fundamentally different manner than electromagnetic radiation, it may be able to provide more accurate estimates of bone strength compared with current densitometric methods. The goal of this research is to significantly improve the effectiveness of current ultrasonic bone assessment techniques by demonstrating the feasibility of a powerful new technology, i.e., neural networks, which can be used in conjunction with ultrasonic measurements, to accurately determine bone density and strength. The specific aims are to measure ultrasonic velocity and attenuation in bovine trabecular bone and use neural networks to determine its bone mineral density and ultimate strength.